Most sounds are transmitted in a normal ear as shown in FIG. 1 through the outer ear 101 to the tympanic membrane (eardrum) 102, which moves the bones of the middle ear 103 (malleus, incus, and stapes) that vibrate the oval window and round window openings of the cochlea 104. The cochlea 104 is a long narrow duct wound spirally about its axis for approximately two and a half turns. It includes an upper channel known as the scala vestibuli and a lower channel known as the scala tympani, which are connected by the cochlear duct. The cochlea 104 forms an upright spiraling cone with a center called the modiolus where the spiral ganglion cells of the acoustic nerve 113 reside. In response to received sounds transmitted by the middle ear 103, the fluid-filled cochlea 104 functions as a transducer to generate electric pulses which are transmitted to the cochlear nerve 113, and ultimately to the brain.
Hearing is impaired when there are problems in the ability to transduce external sounds into meaningful action potentials along the neural substrate of the cochlea 104. To improve impaired hearing, auditory prostheses have been developed. For example, when the impairment is associated with the cochlea 104, a cochlear implant with an implanted stimulation electrode can electrically stimulate auditory nerve tissue with small currents delivered by multiple electrode contacts distributed along the electrode. These electrodes may also be used for sensing neural tissue response signals, i.e. function as measurement electrodes.
In some cases, hearing impairment can be addressed by a cochlear implant (CI), a brainstem-, midbrain- or cortical implant that electrically stimulates auditory nerve tissue with small currents delivered by multiple electrode contacts distributed along an implant electrode. For cochlear implants, the electrode array is inserted into the cochlea. For brain-stem, midbrain and cortical implants, the electrode array is located in the auditory brainstem, midbrain or cortex, respectively.
FIG. 1 shows some components of a typical cochlear implant system where an external microphone provides an audio signal input to an external signal processor 111 which implements one of various known signal processing schemes. For example, signal processing approaches that are well-known in the field of cochlear implants include continuous interleaved sampling (CIS) digital signal processing, channel specific sampling sequences (CSSS) digital signal processing (as described in U.S. Pat. No. 6,348,070, incorporated herein by reference), spectral peak (SPEAK) digital signal processing, fine structure processing (FSP) and compressed analog (CA) signal processing.
The processed signal is converted by the external signal processor 111 into a digital data format, such as a sequence of data frames, for transmission by an external coil 107 into a receiving stimulator processor 108. Besides extracting the audio information, the receiver processor in the stimulator processor 108 may perform additional signal processing such as error correction, pulse formation, etc., and produces a stimulation pattern (based on the extracted audio information) that is sent through electrode lead 109 to an implanted electrode array 110. Typically, the electrode array 110 includes multiple stimulation contacts 112 on its surface that provide selective electrical stimulation of the cochlea 104.
To collect information about the electrode-nerve interface, a commonly used objective measurement is based on the measurement of Neural Action Potentials (NAPs) such as the electrically-evoked Compound Action Potential (eCAP), as described by Gantz et al., Intraoperative Measures of Electrically Evoked Auditory Nerve Compound Action Potentials, American Journal of Otology 15 (2):137-144 (1994), which is incorporated herein by reference. In this approach, the recording electrode is usually placed at the scala tympani of the inner ear. The overall response of the auditory nerve to an electrical stimulus is measured typically very close to the position of the nerve excitation. This neural response is caused by the super-position of single neural responses at the outside of the auditory nerve membranes.
FIG. 2 shows an example of measuring eCAP amplitude based solely on time since stimulation for a single response signal recording. The response signal is characterized by the amplitude between the minimum voltage (this peak is called typically N1) and the maximum voltage (peak is called typically P2), the so-called local extrema. These extrema among others represent the most prominent physiological landmarks of the ECAP signal. The amplitude of the eCAP at the measurement position is in most cases between approximately 10μV and 1800 μV. One eCAP recording paradigm is a so-called “amplitude growth function,” as described by Brown et al., Electrically Evoked Whole Nerve Action Potentials In Ineraid Cochlear Implant Users: Responses To Different Stimulating Electrode Configurations And Comparison To Psychophysical Responses, Journal of Speech and Hearing Research, vol. 39:453-467 (Jun. 1996), which is incorporated herein by reference. This function is the relation between the amplitude of the stimulation pulse and the peak-to-peak voltage of the eCAP.
In the past, relatively simple algorithms were used to determine the latencies of the extrema that represent physiological landmarks; for example, N1 or P2 from single recordings, which often produced physiologically unreasonable values that required manual correction of the determined latencies. Current state of the art methods are based on records of single pulses using only the time since stimulation as the basic factor for fitting functions providing the minima and maxima of the recorded signal. Standard sequences such as amplitude growth functions (AGF), recovery functions (RF) and spread of excitation functions (SoE) are especially affected by the lack of physiologic properties within the model, showing high variation in latencies of extrema or not detecting the extrema for single measurements due to signal artifacts. Artifacts, as understood in this context, are signal components in the recording not arising from physiologic effects that cannot be reduced by averaging multiple recordings.
Sophisticated algorithms are used to reduce the influence of signal artifacts from various sources; for example, alternating stimulation (Eisen M D, Franck K H: “Electrically Evoked Compound Action Potential Amplitude Growth Functions and HiResolution Programming Levels in Pediatric CII Implant Subjects.” Ear & Hearing 2004, 25(6):528-538; incorporated herein by reference in its entirety), masker probe (Brown C, Abbas P, Gantz B: “Electrically evoked whole-nerve action potentials: data from human cochlear implant users.” The Journal of the Acoustical Society of America 1990, 88(3):1385-1391; Miller C A, Abbas P J, Brown C J: An improved method of reducing stimulus artifact in the electrically evoked whole-nerve potential. Ear & Hearing 2000, 21(4):280-290; both incorporated herein by reference in their entirety), tri-phasic stimulation (Zimmerling M: “Messung des elektrisch evozierten Summenaktionspotentials des Hörnervs bei Patienten mit einem Cochlea-Implantat.” In PhD thesis Universität Innsbruck, Institut für Angewandte Physik; 1999; Schoesser H, Zierhofer C, Hochmair E S. Measuring electrically evoked compound action potentials using triphasic pulses for the reduction of the residual stimulation artifact. In: Conference on implantable auditory prostheses; 2001; both of which are incorporated herein by reference in their entirety), scaled template (Miller C A, Abbas P J, Rubinstein J T, Robinson B, Matsuoka A, Woodworth G: Electrically evoked compound action potentials of guinea pig and cat: responses to monopolar, monophasic stimulation. Hearing Research 1998, 119(1-2):142-154; incorporated herein by reference in its entirety), or amplitude template (Brown, C. J.; Hughes, M. L.; Luk, B.; Abbas, P. J.; Wolaver, A. and Gervais, J. “The relationship between EAP and EABR thresholds and levels used to program the nucleus 24 speech processor: data from adults.” Ear Hear, 21(2), pages 151-163, 2000; incorporated herein by reference in its entirety).
Even after applying various artifact reducing methods, the state-of-the art determination of extrema is not very robust (consistent for the whole sequence). The high failure rate and inaccuracy due to inaccurately detected extrema (and consequently eCAP-amplitudes) results in the use of other approaches to determine basic factors which are of interest for implant fitting such as the eCAP-threshold (SmartNRI as used by Advanced Bionics; Arnold, L. & Boyle, P. “SmartNRI: algorithm and mathematical basis.” Proceedings of 8th EFAS Congress/10th Congress of the German Society of Audiology, 2007; (AutoNRT™ as used by Cochlear Ltd.; Botros, A.; van Dijk, B. & Killian, M. “AutoNRT™: An automated system that measures eCAP thresholds with the Nucleus(R) Freedom(tm) cochlear implant via machine intelligence” Artificial Intelligence in Medicine, 2007, 40, 15-28; which are incorporated herein by reference in their entirety).
Despite the weak performance of the fitting functions based only on time since stimulus to correctly determine the response signal amplitude, they still are used by filtering the basic recorded signal and subtracting a disturbing artifact. Standard evaluation procedures are highly affected by the resulting highly variable amplitude values and so need to be manually evaluated, or else specialized procedures can be used to determine subsequent values.